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Effect of 24-hour sleep deprivation on cerebral hemodynamics and cognitive performance

Peter Mukli Andriy Yabluchanskiy Tamas Csipo

Published: April 26, 2021. Version: 1.0.0

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Mukli, P., Yabluchanskiy, A., & Csipo, T. (2021). Effect of 24-hour sleep deprivation on cerebral hemodynamics and cognitive performance (version 1.0.0). PhysioNet.

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Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.


Sleep deprivation (SD) is associated with decreased cognitive performance, but the underlying mechanisms are poorly understood. To assess the impact of SD on cerebral hemodynamics in the frontal lobe, functional near-infrared spectroscopy (fNIRS) measurements were carried out on young subjects. Ten young healthy adults (males, age of 27.6±3.7 years, one left-handed participant) were recruited for this study conducted in 2018 at University of Oklahoma, Translational Geroscience Laboratory. The examination protocol began with assessment of cognitive performance with the aid of Cambridge Neuropsychological Test Automated Battery (CANTAB). The following tests were used: motor screening task, rapid visual processing, reaction time, spatial working memory, paired associated learning, and delayed matching to visual sample. Subsequently fNIRS measurements were taken from participants while carrying out a finger-tapping exercise. Each subject was presented with an auditory command to perform a finger-tapping task over three sets of 10 seconds using the left index finger and three sets of 10 seconds using the right index finger, with 10 seconds inter-stimulus periods between each task. Functional NIRS recordings were acquired using a NIRScout platform (NIRx Medical Technologies LLC, NY, USA). Relative oxy- and deoxyhemoglobin time series can be obtained from measured intensities by applying differential modified Beer Lamber Law. All measurements were performed before and after 24 hours of SD. The dataset forms the basis for two studies that explore: (1) the relationship between sleep deprivation, cognitive performance, and neurovascular coupling response in young healthy adults; and (2) sleep deprivation and functional connectivity of the frontal cortex.


Sleep deprivation (SD) has a high prevalence (32-39%) among 18-65 years old adults and has significant impact to the public health and economy. Short sleep duration (<7 hours) is strongly associated with fatigue, impaired concentration, depression and an overall decreased cognitive performance [1-2]. Despite its prevalence and clinical importance, the mechanisms underlying sleep deprivation-induced decline in cognitive performance are poorly understood.

Several functional neuroimaging studies have reported global and local decreases of neural activity in the state of sleep deprivation. Neuronal activity can be assessed indirectly by analyzing the functional hemodynamic responses locally evoked by neurovascular coupling (NVC) mechanisms [3]. NVC responses can be detected by functional near-infrared spectroscopic (fNIRS) measurements, a non-invasive optical imaging technique [4].

With the aid of fNIRS measurements, we are also able to assess functional connectivity (FC) of the brain that describes dynamic connections between brain regions with the aid of measures capturing the statistical relationship of the corresponding neurophysiological processes recorded simultaneously [5]. As far as macroscopic scales concerned, sleep deprivation-induced deficits in attention and executive function imply the specific involvement of the prefrontal cortex (PFC). These cognitive processes also require the cooperation of brain regions that give rise to specific networks dedicated to higher order mental function.

In order to better understand the mechanisms contributing to sleep deprivation-induced changes in functional connections, NVC responses and their link to impaired cognitive performance in real-life situations (e.g. decision making), additional studies are needed. Importantly, studying task modulations of brain connectivity is essential. Changes in functional connectivity can be detected with functional near infrared spectroscopy (fNIRS), whose advantage lies in its ability to directly capture local changes in cerebral hemodynamics by continuously monitoring concentrations of oxygenated and deoxygenated hemoglobin in the cerebral cortex. In this project, we share a dataset that enables investigations into the impact of sleep deprivation on cerebral hemodynamics.


Functional NIRS measurements were performed using a NIRScout platform (NIRx Medical Technologies LLC, NY, USA). The system was equipped with 16 sources (F3, AF7, AF3, Fz, Fpz, AF4, F4, AF8, FC6, C4, FC2, CP2, FC1, CP1, C3, FC5) emitting light at two different wavelengths (760 and 850nm) and 16 photodetectors (F5, F1, Fp1, AFz, F2, Fp2, F6, AFF6h, C6, CC4, CP4, C2, C1, FC3, CP3, C5) defining 48 channels.

Detailed definition of channels: Channel 1: F3-F5, Channel 2: F3-F1, Channel 3: F3-FC3, Channel 4: AF7-F5, Channel 5: AF7-Fp1, Channel 6: AF3-F5, Channel 7: AF3-F1, Channel 8: AF3-Fp1, Channel 9: AF3-AFz, Channel 10: Fz-F1, Channel 11: Fz-AFz, Channel 12: Fz-F2, Channel 13: Fpz-Fp1, Channel 14: Fpz-AFz, Channel 15: Fpz-Fp2, Channel 16: AF4-AFz, Channel 17: AF4-F2, Channel 18: AF4-Fp2, Channel 19: AF4-F6, Channel 20: F4-F2, Channel 21: F4-F6, Channel 22: F4-CC4, Channel 23: AF8-Fp2, Channel 24: AF8-F6, Channel 25: FC6-F6, Channel 26: FC6-C6, Channel 27: FC6-CC4, Channel 28: C4-C6, Channel 29: C4-CC4, Channel 30: C4-CP4, Channel 31: C4-C2, Channel 32: FC2-F2, Channel 33: FC2-CC4, Channel 34: FC2-C2, Channel 35: CP2-CP4, Channel 36: CP2-C2, Channel 37: FC1-F1, Channel 38: FC1-C1, Channel 39: FC1-FC3, Channel 40: CP1-C1, Channel 41: CP1-CP3, Channel 42: C3-C1, Channel 43: C3-FC3, Channel 44: C3-CP3, Channel 45: C3-C5, Channel 46: FC5-F5, Channel 47: FC5-FC3, Channel 48: FC5-C5.

Functional NIRS examinations were performed using the NIRScout platform (NIRx Medical Technologies LLC, NY, USA) equipped with 16 light source and 16 detector optodes. A128-port Easycap headcap (Easycap GmbH, Woerthsee-Etterschlag, Germany) was positioned over the head to cover the area of the international 10-10 system. The line between Fpz and Iz ports on the headcap was aligned with the sagittal plane of the head, and the optode in the Fpz position of the cap was aligned with Fpz on the subject. The cap was set up with custom spacers that limit the variability of distance between optodes to average source-detector separation of 3 cm. The placement of optodes covered the prefrontal cortex, dorsolateral prefrontal cortex, and also included the medial motor cortex. Sufficient coverage of these regions was determined by projection of channel position to the cortical surface within the Montreal Neurological Institute coordinate space (Supplemental Table 1) [7-8].

To evoke NVC responses during fNIRS recording, the finger tapping task was used. Briefly, with both hands rested on the desk in front of them, participants were presented with auditory command to start tapping either their left or right index finger against the surface of the desk. After a one minute baseline resting period, subjects had to perform three sets of 10 second tapping using the left index finger (Stim1, Stim3 and Stim5) and three sets of 10 seconds tapping using the right (Stim2, Stim4 and Stim6) index finger. There were 10 second inter-stimulus periods between each task, and the total duration of the measurement was approximately 3 minutes.

We determined the functional brain networks using preprocessed HbT time series, as described in the paper of Racz et al. [4]. We assessed cognitive performance with the aid of Cambridge Neuropsychological Test Automated Battery (CANTAB). The following tests were used: motor screening task, rapid visual processing, reaction time, spatial working memory, paired associated learning, and delayed matching to visual sample.

Measurements were carried out before and after 24-hour sleep deprivation in the Translational Geroscience Laboratory of Oklahoma University (Center for Healthy Brain Aging), in a quiet and dimly lit room. All participants performed cognitive tests in an uninterrupted environment using the touchscreen 10.5” iOS tablet device running the CANTAB application.

The study protocol was approved by the Institutional Review Board of the University of Oklahoma Health Sciences Center and was conducted in compliance with the Helsinki Declaration.

Data Description

  • Raw intensity data and metainformation from near-infrared spectroscopy measurements before and after sleep deprivation as .wl1 and .wl2 files (ASCII text file, tabulated data). .wl1 files refer to near-infrared light intensities measured at 760 nm. wl2 files refer  to near-infrared light intensities measured at 850 nm
  • Meta-information about the measurement: marker positions, channel layout and other details are described in txt files and shared as ASCII text format (following file extension: .evt, .hdr, .tpl, .inf, .set)
  • Results of cognitive tests before and after sleep deprivation are stored as comma-separated (.csv) values along with a description of each measure and their order in the CSV file.
  • MAT files contain information about montage, probe, channel layout and can be viewed in MATLAB and Octave. These files are recommended for NIRS analysis with AnalyzIR toolbox of MATLAB [6]. All of these information are also provided in this description and ASCII text files as mentioned above.

Usage Notes

The recommended software for data analysis is MATLAB (version 2016 or newer) with Wavelet Toolbox and AnalyzIR toolbox.

This dataset was collected for two studies that explored: (1) the relationship between sleep deprivation, cognitive performance, and neurovascular coupling response in young healthy adults; and (2) sleep deprivation and functional connectivity of the frontal cortex.

Further features of NIRS data could be explored to gain insight into what parameters of cerebral hemodynamics are most sensitive to sleep deprivation and what resting- or task-state parameters derived from NIRS signals show the strongest correlation with sleep-deprivation related change in cognitive performance. Specifically, the following tasks could be explored:

  • What is the impact of sleep deprivation on cognitive performance assessed by CANTAB tests?
  • How does sleep deprivation affect neurovascular coupling responses?
  • How are neuropsychological test scores associated with parameters determined from fNIRS data reflecting neurovascular coupling responses and functional connectivity?
  • How do local and global properties of functional brain networks of the frontal lobe change 24 hour after sleep deprivation?
  • How does sleep deprivation affect task related reorganization of functional brain networks during administration of finger tapping exercises?


  • Small sample size: n=9 (after exclusion of one subject due to poor quality of NIRS signals).
  • Although the shared data meet usual quality control requirements (more than 80% of channels show less than 7.5% coefficient of variance in the corresponding intensity signals) data should be preprocessed to eliminate artifacts.
  • In addition to small sample size, inefficient recruitment of neural networks may lead to similar decreases of NVC that were detected, and our study did not allow us to investigate the effect of sleep deprivation on other components of neurovascular unit, such as neuronal activity.


We gratefully acknowledge the participation of the subjects in our study.

Conflicts of Interest

The authors declare no conflicts of interest.


  1. Nir Y, Andrillon T, Marmelshtein A, Suthana N, Cirelli C, Tononi G, et al. Selective neuronal lapses precede human cognitive lapses following sleep deprivation. Nature medicine. 2017;23(12):1474-80.
  2. Krause AJ, Simon EB, Mander BA, Greer SM, Saletin JM, Goldstein-Piekarski AN, et al. The sleep-deprived human brain. Nature reviews Neuroscience. 2017;18(7):404-18.
  3. Girouard H, Iadecola C. Neurovascular coupling in the normal brain and in hypertension, stroke, and Alzheimer disease. J Appl Physiol. 2006;100(1):328-35.
  4. Csipo T, Mukli P, Lipecz A, Tarantini S, Bahadli D, Abdulhussein O, et al. Assessment of age-related decline of neurovascular coupling responses by functional near-infrared spectroscopy (fNIRS) in humans. GeroScience. 2019.
  5. Racz FS, Mukli P, Nagy Z, Eke A. Increased prefrontal cortex connectivity during cognitive challenge assessed by fNIRS imaging. Biomedical Optics Express. 2017;8(8):3842-55.
  6. Santosa H, Zhai XT, Fishburn F, Huppert T. The NIRS Brain AnalyzIR Toolbox. Algorithms. 2018;11(5).
  7. Aasted CM, Yucel MA, Cooper RJ, Dubb J, Tsuzuki D, Becerra L, et al. Anatomical guidance for functional near-infrared spectroscopy: AtlasViewer tutorial. Neurophotonics. 2015;2(2):020801.
  8. Tsuzuki D, Cai D, Dan H, Kyutoku Y, Fujita A, Watanabe E, et al. Stable and convenient spatial registration of stand-alone NIRS data through anchor-based probabilistic registration. Neurosci Res. 2012;72(2):163-71.


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